Research Engineer – Distributed AI Infrastructure

ByteDance; San Jose (CA); On-site; USD 254,000 - 480,000; Full time

Benefits offered by this job

Medical/Dental/Vision insurance

401(k) with company match

Parental leave

Disability insurance

Life insurance

Paid holidays

Paid sick days

Paid Personal Time

Job summary

ByteDance Seed in San Jose is seeking PhD‑level researchers to advance large‑scale AI infrastructure, including distributed training, RL frameworks, and multimodal model work.

You will collaborate with researchers and engineers to translate research prototypes into production‑ready systems, optimize GPU and network throughput, and build observability tools.

This role offers bold problems, strong growth, and the chance to shape foundational AI technologies for real products.

Qualifications

• PhD in Computer Science, Electrical Engineering, Electrical and Computer Engineering, Physics, Mathematics, or a related discipline.
• Strong background in distributed systems, large-scale machine learning systems, or deep learning infrastructure.
• Research or hands‑on experience in training or optimizing large-scale models (e.g., LLMs, multimodal models, RL systems).
• Understanding of parallelism strategies (e.g., data, model/tensor, pipeline, expert parallelism) and distributed training concepts.
• Familiarity with reinforcement learning workflows such as rollout generation, policy optimization, and evaluation loops.
• Proficiency in programming (e.g., Python and/or C++) and experience with modern ML frameworks (e.g., PyTorch and distributed training tools).

Responsibilities

• Conduct research and development on large-scale AI infrastructure to support efficient training and post-training of foundation models, multimodal LLMs, and image/video generation models.
• Design and optimize distributed training strategies, including data/model/tensor/pipeline/expert parallelism, computation–communication overlap, and large-scale GPU cluster scaling.
• Prototype and improve end-to-end reinforcement learning (RL) training systems, covering rollout generation, policy optimization, evaluation, and iterative deployment workflows.
• Build scalable and fault-tolerant infrastructure that operates reliably under dynamic workloads and heterogeneous compute environments.
• Analyze performance bottlenecks across the training stack (e.g., networking, scheduling, GPU memory management), and develop principled optimization approaches to improve throughput, efficiency, and stability.
• Develop tooling, monitoring, debugging, and observability frameworks to ensure reliability of large-scale training and RL systems.
• Collaborate with researchers and engineers on system–algorithm co-design, translating research prototypes into scalable, production-ready infrastructure systems.

Skills

Distributed systems

Large-scale ML

RL workflows

Python

C++

PyTorch

Education

PhD in Computer Science, Electrical Engineering, or related field

Tools

Distributed training tools

Job description

ByteDance Seed in San Jose is seeking PhD‑level researchers to advance large‑scale AI infrastructure, including distributed training, RL frameworks, and multimodal model work.

You will collaborate with researchers and engineers to translate research prototypes into production‑ready systems, optimize GPU and network throughput, and build observability tools.

This role offers bold problems, strong growth, and the chance to shape foundational AI technologies for real products.